KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization
arXiv:2603.10085v1 Announce Type: new Abstract: Improving GPU kernel efficiency is crucial for advancing AI systems. Recent work has explored leveraging large language models (LLMs) for GPU kernel generation and optimization. However, existing LLM-based kernel optimization pipelines typically rely on opaque, implicitly learned heuristics within the LLMs to determine optimization strategies. This leads to inefficient trial-and-error and weakly interpretable optimizations. Our key insight is to replace implicit heuristics with expert optimization skills that are knowledge-driven and aware of task trajectories. Specifically, we present KernelSkill, a multi-agent framework with a dual-level memory architecture. KernelSkill operates by coordinating agents with long-term memory of reusable expert skills and short-term memory to prevent repetitive backtracking. On KernelBench Levels 1-3, KernelSkill achieves a 100% success rate and average speedups of 5.44x, 2.82x, and 1.92x over Torch Eager
arXiv:2603.10085v1 Announce Type: new Abstract: Improving GPU kernel efficiency is crucial for advancing AI systems. Recent work has explored leveraging large language models (LLMs) for GPU kernel generation and optimization. However, existing LLM-based kernel optimization pipelines typically rely on opaque, implicitly learned heuristics within the LLMs to determine optimization strategies. This leads to inefficient trial-and-error and weakly interpretable optimizations. Our key insight is to replace implicit heuristics with expert optimization skills that are knowledge-driven and aware of task trajectories. Specifically, we present KernelSkill, a multi-agent framework with a dual-level memory architecture. KernelSkill operates by coordinating agents with long-term memory of reusable expert skills and short-term memory to prevent repetitive backtracking. On KernelBench Levels 1-3, KernelSkill achieves a 100% success rate and average speedups of 5.44x, 2.82x, and 1.92x over Torch Eager on Levels 1, 2, and 3, respectively, outperforming prior baselines. Code is available at https://github.com/0satan0/KernelMem/.
Executive Summary
The article presents KernelSkill, a multi-agent framework for GPU kernel optimization. It replaces implicit heuristics with expert optimization skills, utilizing a dual-level memory architecture to coordinate agents with long-term and short-term memory. KernelSkill achieves a 100% success rate and significant speedups over prior baselines on KernelBench Levels 1-3. This framework has the potential to advance AI systems by improving GPU kernel efficiency.
Key Points
- ▸ KernelSkill is a multi-agent framework for GPU kernel optimization
- ▸ It utilizes a dual-level memory architecture for coordinating agents
- ▸ The framework achieves significant speedups over prior baselines on KernelBench Levels 1-3
Merits
Improved Efficiency
KernelSkill's expert optimization skills and dual-level memory architecture enable more efficient GPU kernel optimization
Interpretability
The framework's knowledge-driven approach provides more interpretable optimizations compared to existing LLM-based pipelines
Demerits
Complexity
The multi-agent framework and dual-level memory architecture may introduce additional complexity and require significant computational resources
Expert Commentary
KernelSkill represents a significant advancement in GPU kernel optimization, offering a more efficient and interpretable approach compared to existing LLM-based pipelines. The framework's dual-level memory architecture and expert optimization skills enable more effective coordination of agents, resulting in improved performance and efficiency. However, the complexity of the framework and potential computational resource requirements must be carefully considered. Further research is needed to fully explore the potential of KernelSkill and its applications in various AI domains.
Recommendations
- ✓ Further evaluation of KernelSkill on more complex benchmarks and real-world AI applications
- ✓ Investigation of potential integrations with existing AI systems and techniques to enhance overall performance and efficiency